class_docstr: An implementation of `"Label Propagation Clustering" <https://arxiv.org/abs/0709.2938>`_
  from the Physical Review '07 paper "Near Linear Time Algorithm to Detect Community Structures
  in Large-Scale Networks". The tool executes a series of label propagations with unique labels.
  The final labels are used as cluster memberships.
  
  Args:
      seed (int): Random seed. Default is 42.
      iterations (int): Propagation iterations. Default is 100.
docstr: Getting the cluster membership of nodes.
  
  Return types:
      * **memberships** *(dict)* - Node cluster memberships.
docstr_tok: ["Getting","the","cluster","membership","of","nodes",".","Return","types",":","*","*","*","memberships","*","*","*","(","dict",")","*","-","Node","cluster","memberships","."]
code: def get_memberships(self) -> Dict[int, int]:
      r"""Getting the cluster membership of nodes.
  
      Return types:
          * **memberships** *(dict)* - Node cluster memberships.
      """
      memberships = self._labels
      return memberships
  
file_path: karateclub/karateclub/community_detection/non_overlapping/label_propagation.py
imports: import random
  import networkx
  from typing import Dict
  from karateclub.estimator import Estimator
label_desc: Use image node_id 5 for calling the LabelPropagation obj's underlying member method code with example usage: obj.get_memberships() and returns: memberships